Test-time adaptation (TTA) has emerged as a promising solution to address
performance decay due to unforeseen distribution shifts between training and
test data. While recent TTA methods excel in adapting to test data variations,
such adaptability exposes a model to vulnerability against malicious examples,
an aspect that has received limited attention. Previous studies have uncovered
security vulnerabilities within TTA even when a small proportion of the test
batch is maliciously manipulated. In response to the emerging threat, we
propose median batch normalization (MedBN), leveraging the robustness of the
median for statistics estimation within the batch normalization layer during
test-time inference. Our method is algorithm-agnostic, thus allowing seamless
integration with existing TTA frameworks. Our experimental results on benchmark
datasets, including CIFAR10-C, CIFAR100-C and ImageNet-C, consistently
demonstrate that MedBN outperforms existing approaches in maintaining robust
performance across different attack scenarios, encompassing both instant and
cumulative attacks. Through extensive experiments, we show that our approach
sustains the performance even in the absence of attacks, achieving a practical
balance between robustness and performance.

测试时间适应（TTA）已成为解决测试数据训练和测试之间意外分布变化导致性能下降的有希望的方法。本研究提出中位数批归一化（MedBN）来应对 TTA 中的恶意示例对模型造成的潜在威胁，通过在测试推理期间的批归一化层中利用中位数来进行统计估计。我们的实验结果表明，MedBN 在维护不同攻击场景下的鲁棒性表现方面优于现有方法，且在无攻击情况下仍能保持良好的性能。

MedBN: 对恶意测试样本的鲁棒测试时适应性

MedBN: Robust Test-Time Adaptation against Malicious Test Samples

Machine learning (ML) plays a pivotal role in detecting malicious software.
Despite the high F1-scores reported in numerous studies reaching upwards of
0.99, the issue is not completely solved. Malware detectors often experience
performance decay due to constantly evolving operating systems and attack
methods, which can render previously learned knowledge insufficient for
accurate decision-making on new inputs. This paper argues that commonly
reported results are inflated due to two pervasive sources of experimental bias
in the detection task: spatial bias caused by data distributions that are not
representative of a real-world deployment; and temporal bias caused by
incorrect time splits of data, leading to unrealistic configurations. To
address these biases, we introduce a set of constraints for fair experiment
design, and propose a new metric, AUT, for classifier robustness in real-world
settings. We additionally propose an algorithm designed to tune training data
to enhance classifier performance. Finally, we present TESSERACT, an
open-source framework for realistic classifier comparison. Our evaluation
encompasses both traditional ML and deep learning methods, examining published
works on an extensive Android dataset with 259,230 samples over a five-year
span. Additionally, we conduct case studies in the Windows PE and PDF domains.
Our findings identify the existence of biases in previous studies and reveal
that significant performance enhancements are possible through appropriate,
periodic tuning. We explore how mitigation strategies may support in achieving
a more stable and better performance over time by employing multiple strategies
to delay performance decay.

本论文通过引入公平实验设计的一组约束条件和 AUT 度量，提出了解决恶意软件检测任务中实验偏差的方法，并提供了一个能够增强分类器性能的算法，以及一个用于实际分类器比较的开源框架 TESSERACT。研究发现以前的研究存在偏差，并通过适时调整的多种策略来延缓性能下降，从而实现更稳定和更好的性能。

TESSERACT: 消除恶意软件分类中的实验偏见 —— 跨时空的研究（扩展版）

TESSERACT: Eliminating Experimental Bias in Malware Classification  across Space and Time (Extended Version)

Preventing the performance decay of Transformers on inputs longer than those
used for training has been an important challenge in extending the context
length of these models. Though the Transformer architecture has fundamentally
no limits on the input sequence lengths it can process, the choice of position
encoding used during training can limit the performance of these models on
longer inputs. We propose a novel functional relative position encoding with
progressive interpolation, FIRE, to improve Transformer generalization to
longer contexts. We theoretically prove that this can represent some of the
popular relative position encodings, such as T5's RPE, Alibi, and Kerple. We
next empirically show that FIRE models have better generalization to longer
contexts on both zero-shot language modeling and long text benchmarks.

使用功能性相对位置编码与渐进插值方法 (FIRE) 对 Transformers 进行训练，在处理更长的上下文时具有更好的泛化性能。

相对位置的功能内插改进长上下文 Transformer

Functional Interpolation for Relative Positions Improves Long Context  Transformers

In many practical applications, it is often difficult and expensive to obtain
enough large-scale labeled data to train deep neural networks to their full
capability. Therefore, transferring the learned knowledge from a separate,
labeled source domain to an unlabeled or sparsely labeled target domain becomes
an appealing alternative. However, direct transfer often results in significant
performance decay due to domain shift. Domain adaptation (DA) addresses this
problem by minimizing the impact of domain shift between the source and target
domains. Multi-source domain adaptation (MDA) is a powerful extension in which
the labeled data may be collected from multiple sources with different
distributions. Due to the success of DA methods and the prevalence of
multi-source data, MDA has attracted increasing attention in both academia and
industry. In this survey, we define various MDA strategies and summarize
available datasets for evaluation. We also compare modern MDA methods in the
deep learning era, including latent space transformation and intermediate
domain generation. Finally, we discuss future research directions for MDA.

本文介绍了多源域适应的概念和相关技术方法，探讨了其在深度学习时代的发展趋势和研究方向，为该领域的研究提供借鉴和参考。